Taguchi design of experiments nov 24 2013
Upcoming SlideShare
Loading in...5
×

Like this? Share it with your network

Share

Taguchi design of experiments nov 24 2013

  • 648 views
Uploaded on

design of experiments ...

design of experiments
anova

More in: Technology , Education
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
No Downloads

Views

Total Views
648
On Slideshare
648
From Embeds
0
Number of Embeds
0

Actions

Shares
Downloads
43
Comments
0
Likes
1

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 1. TAGUCHI DESIGN OF EXPERIMENTS Prof. Charlton S. Inao
  • 2. • 2.2.1 Definition • Taguchi has envisaged a new method of conducting the design of experiments which are based on well defined guidelines. This method uses a special set of arrays called orthogonal arrays. These standard arrays stipulates the way of conducting the minimal number of experiments which could give the full information of all the factors that affect the performance parameter. The crux of the orthogonal arrays method lies in choosing the level combinations of the input design variables for each experiment.
  • 3. Assumptions of the Taguchi method • The additive assumption implies that the individual or main effects of the independent variables on performance parameter are separable. Under this assumption, the effect of each factor can be linear, quadratic or of higher order, but the model assumes that there exists no cross product effects (interactions) among the individual factors. That means the effect of independent variable 1 on performance parameter does not depend on the different level settings of any other independent variables and vice versa. If at anytime, this assumption is violated, then the additivity of the main effects does not hold, and the variables interact.
  • 4. Designing an experiment • The design of an experiment involves the following steps 1. Selection of independent variables 2. Selection of number of level settings for each independent variable 3. Selection of orthogonal array 4. Assigning the independent variables to each column 5. Conducting the experiments 6. Analyzing the data Inference
  • 5. Selection of the independent variables • Before conducting the experiment, the knowledge of the product/process under investigation is of prime importance for identifying the factors likely to influence the outcome. In order to compile a comprehensive list of factors, the input to the experiment is generally obtained from all the people involved in the project.
  • 6. Deciding the number of levels • Once the independent variables are decided, the number of levels for each variable is decided. The selection of number of levels depends on how the performance parameter is affected due to different level settings. If the performance parameter is a linear function of the independent variable, then the number of level setting shall be 2. However, if the independent variable is not linearly related, then one could go for 3, 4 or higher levels depending on whether the relationship is quadratic, cubic or higher order. In the absence of exact nature of relationship between the independent variable and the performance parameter, one could choose 2 level settings. After analyzing the experimental data, one can decide whether the assumption of level setting is right or not based on the percent contribution and the error calculations.
  • 7. Selection of an orthogonal array • • Before selecting the orthogonal array, the minimum number of experiments to be conducted shall be fixed based on the total number of degrees of freedom [5] present in the study. The minimum number of experiments that must be run to study the factors shall be more than the total degrees of freedom available. In counting the total degrees of freedom the investigator commits 1 degree of freedom to the overall mean of the response under study. The number of degrees of freedom associated with each factor under study equals one less than the number of levels available for that factor. Hence the total degrees of freedom without interaction effect is 1 + as already given by equation 2.1. For example, in case of 11 independent variables, each having 2 levels, the total degrees of freedom is 12. Hence the selected orthogonal array shall have at least 12 experiments. An L12 orthogonal satisfies this requirement. Once the minimum number of experiments is decided, the further selection of orthogonal array is based on the number of independent variables and number of factor levels for each independent variable.
  • 8. Assigning the independent variables to columns • The order in which the independent variables are assigned to the vertical column is very essential. In case of mixed level variables and interaction between variables, the variables are to be assigned at right columns as stipulated by the orthogonal array [3]. • Finally, before conducting the experiment, the actual level values of each design variable shall be decided. It shall be noted that the significance and the percent contribution of the independent variables changes depending on the level values assigned. It is the designers responsibility to set proper level values.
  • 9. Conducting the experiment • Once the orthogonal array is selected, the experiments are conducted as per the level combinations. It is necessary that all the experiments be conducted. The interaction columns and dummy variable columns shall not be considered for conducting the experiment, but are needed while analyzing the data to understand the interaction effect. The performance parameter under study is noted down for each experiment to conduct the sensitivity analysis.
  • 10. Analysis of the data • • Since each experiment is the combination of different factor levels, it is essential to segregate the individual effect of independent variables. This can be done by summing up the performance parameter values for the corresponding level settings. For example, in order to find out the main effect of level 1 setting of the independent variable 2 (refer Table 2.1), sum the performance parameter values of the experiments 1, 4 and 7. Similarly for level 2, sum the experimental results of 2, 5 and 7 and so on. Once the mean value of each level of a particular independent variable is calculated, the sum of square of deviation of each of the mean value from the grand mean value is calculated. This sum of square deviation of a particular variable indicates whether the performance parameter is sensitive to the change in level setting. If the sum of square deviation is close to zero or insignificant, one may conclude that the design variables is not influencing the performance of the process. In other words, by conducting the sensitivity analysis, and performing analysis of variance (ANOVA), one can decide which independent factor dominates over other and the percentage contribution of that particular independent variable. The details of analysis of variance is dealt in chapter 5.
  • 11. Inference • From the above experimental analysis, it is clear that the higher the value of sum of square of an independent variable, the more it has influence on the performance parameter. One can also calculate the ratio of individual sum of square of a particular independent variable to the total sum of squares of all the variables. This ratio gives the percent contribution of the independent variable on the performance parameter. • In addition to above, one could find the near optimal solution to the problem. This near optimum value may not be the global optimal solution. However, the solution can be used as an initial / starting value for the standard optimization technique.
  • 12. • Once the experiments are conducted, the program automatically stores the process parameters and the corresponding experiment number and level combination of all the design variables in the blackboard. This raw data has been processed further to segregate the main effect of each individual variable. The following are the important parameters which the program automatically calculates. • i) Mean value of each level of a design variable • ii) Sum of square value of the design variables • iii) Total sum of square • iv) Percent contribution • v) Near optimal value of the objective function • vi) Confirmation test • vii) ANOVA (Analysis of Variance) test
  • 13. • It shall be noted that the grand mean of all the experiments is the same as the average of the mean values of each level of a design variable as shown in Figure 5.5. Based on the mean values of each design variable, the sensitivity analysis is performed. • Sum of square value • The sum of square of individual design variable can be calculated using either of the following equations
  • 14. • where L is the number of level, N is the number of experiments conducted, R is the no of repetition per level which equals , T is the sum of process parameters of all the experiments, ......is the grand mean value of all the experiments which equals , and ...... is the mean value of jth level value of ith variable. • In case of L9 array which is given in Table 2.1, the total sum of square of variable 3 can be calculated using the equation 5.5 or 5.6.
  • 15. • Similarly the sum of square values for other variables can also be found. Total sum of square The total sum of square (SSTO) is the sum of deviation of the experimental process parameters from the grand mean value of the experiment. This can be obtained from the equations 5.7 and 5.8.
  • 16. where .... is the performance parameters for the kth experiment.
  • 17. • • • • • • • • This total sum of square need not be the same as the total of sum of square of each individual variables. This is either due to the interaction effect between the design variables or due to the introduction of dummy variables, if any. Percent contribution The percent contribution of each design variable is the ratio of the sum of squares of a particular design variable to the total sum of square of all the variables. This ratio indicates the influence of the design variable over the performance parameter due to the change in the level settings. Near optimal level value In order to find the near optimal value of the objective function, a new experiment is conducted by setting the near optimum level for each design variable. The near optimum level for any design variable can be easily found from the mean values of all the level. The optimum level values can be used as the initial value for further optimization problem. ANOVA (Analysis of Variance) test It may be noted from the previous sections that the significance of individual design variables can be found from the percentage contribution. But it is not possible to categorically judge from the contribution value whether 5% contribution is significant or not. Using analysis of variance (ANOVA) approach, one can accept or reject a independent variable from the analysis given the confidence level, . This can be done by conducting F-test [1]. As per the F-test, a variable is significant only if the ratio of mean sum of square of a variable (MSV) to mean sum of square of error (MSE) is greater than the calculated F-value. The calculation of MSV and MSE is based on the accumulation method [1] as given by the following equations.
  • 18. • the calculated F-value is based on the statistical approach which obeys f-distribution with L-1 numerator degrees of freedom, N-L denominator degrees of freedom and as confidence level. The hypothesis for accepting or rejecting the significance of a variable is given by the following rules. • Null Hypothesis (Ho) : The design variable is not significant (5.11a) • Alternate Hypothesis (Ha) : The design variable is significant (5.11b)